-------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_3.log
  log type:  text
 opened on:  11 May 2023, 12:39:16

.         
. ******************************************************************** 
. ********      TABLE 3:  GQR BY DECADE                       ********
. ********************************************************************    
. 
.         
. use "Duranton_Turner_AER_2010.dta", clear

. 
. 
.         * Selection of observations
.         drop if l_ln_km_IH_83==0
(47 observations deleted)

. 
.         rename sprawl_1992 sprawl_1993

.         rename sprawl_1976 sprawl_1983

.         gen sprawl_2003 = sprawl_1993

. 
.         *rescale so display is better
.         replace elevat_range_msa = elevat_range_msa/1000 
variable elevat_range_msa was int now float
(228 real changes made)

.         replace ruggedness_msa = ruggedness_msa/1000
(228 real changes made)

.         replace heating_dd = heating_dd/100
(228 real changes made)

.         replace cooling_dd = cooling_dd/100
(228 real changes made)

. 
.       rename S_somecollege_80 S_somecollege_1983

.       rename S_somecollege_00 S_somecollege_2003

.       rename S_somecollege_90 S_somecollege_1993

.       rename S_poor_80 S_poor_1983

.       rename S_poor_90 S_poor_1993

.       rename S_poor_00 S_poor_2003

.       rename l_mean_income_80 l_mean_income_1983

.       rename l_mean_income_90 l_mean_income_1993

.       rename l_mean_income_00 l_mean_income_2003

.       rename S_manuf83 S_manuf_1983

.       rename S_manuf93 S_manuf_1993

.       rename S_manuf03 S_manuf_2003

.       rename S_truck83 S_truck_1983

.       rename S_truck93 S_truck_1993

.       rename S_truck03 S_truck_2003

.       gen l_pop_1983 = l_pop80 

.       gen l_pop_1993 = l_pop90 

.       gen l_pop_2003 = l_pop00 

. 
.       rename l_max_84bus l_bus_1983 

.       rename l_max_94bus l_bus_1993 

.       rename l_max_04bus l_bus_2003 

.       rename l_transit84 l_transit_1983

.       rename l_transit94 l_transit_1993 

.       rename l_transit04 l_transit_2003

. 
.       rename l_ln_km_IHU_83 l_ln_km_IHU_1983

.       rename l_ln_km_IHU_93 l_ln_km_IHU_1993

.       rename l_ln_km_IHU_03 l_ln_km_IHU_2003

.       rename l_ln_km_IH_83 l_ln_km_IH_1983

.       rename l_ln_km_IH_93 l_ln_km_IH_1993

.       rename l_ln_km_IH_03 l_ln_km_IH_2003

.       rename l_ln_km_MRU_83 l_ln_km_MRU_1983

.       rename l_ln_km_MRU_93 l_ln_km_MRU_1993

.       rename l_ln_km_MRU_03 l_ln_km_MRU_2003

.       
.       rename l_vmt_IHU_83 l_vmt_IHU_1983

.       rename l_vmt_IHU_93 l_vmt_IHU_1993

.       rename l_vmt_IHU_03 l_vmt_IHU_2003

.       rename l_vmt_IH_83 l_vmt_IH_1983

.       rename l_vmt_IH_93 l_vmt_IH_1993

.       rename l_vmt_IH_03 l_vmt_IH_2003

.       rename l_vmt_MRU_83 l_vmt_MRU_1983

.       rename l_vmt_MRU_93 l_vmt_MRU_1993

.       rename l_vmt_MRU_03 l_vmt_MRU_2003

. 
. 
. 
.         reshape long l_ln_km_IH  l_ln_km_IHU  l_ln_km_IHNU  l_ln_km_MRU l_vmt_IH l_
> vmt_IHU  l_vmt_IHNU  l_vmt_MRU l_bus l_transit sprawl S_somecollege l_mean_income S
> _poor S_manuf S_truck l_pop, i(msa ) j(year _1983 _1993 _2003)
(variable l_ln_km_IHNU_1983 not found)
(variable l_vmt_IHNU_1983 not found)
(variable l_ln_km_IHNU_1993 not found)
(variable l_vmt_IHNU_1993 not found)
(variable l_ln_km_IHNU_2003 not found)
(variable l_vmt_IHNU_2003 not found)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              228   ->   684         
Number of variables                 217   ->   190         
j variable (3 values)                     ->   year
xij variables:
l_ln_km_IH_1983 l_ln_km_IH_1993 l_ln_km_IH_2003->l_ln_km_IH
l_ln_km_IHU_1983 l_ln_km_IHU_1993 l_ln_km_IHU_2003->l_ln_km_IHU
l_ln_km_IHNU_1983 l_ln_km_IHNU_1993 l_ln_km_IHNU_2003->l_ln_km_IHNU
l_ln_km_MRU_1983 l_ln_km_MRU_1993 l_ln_km_MRU_2003->l_ln_km_MRU
l_vmt_IH_1983 l_vmt_IH_1993 l_vmt_IH_2003 ->   l_vmt_IH
l_vmt_IHU_1983 l_vmt_IHU_1993 l_vmt_IHU_2003-> l_vmt_IHU
l_vmt_IHNU_1983 l_vmt_IHNU_1993 l_vmt_IHNU_2003->l_vmt_IHNU
l_vmt_MRU_1983 l_vmt_MRU_1993 l_vmt_MRU_2003-> l_vmt_MRU
       l_bus_1983 l_bus_1993 l_bus_2003   ->   l_bus
l_transit_1983 l_transit_1993 l_transit_2003-> l_transit
    sprawl_1983 sprawl_1993 sprawl_2003   ->   sprawl
S_somecollege_1983 S_somecollege_1993 S_somecollege_2003->S_somecollege
l_mean_income_1983 l_mean_income_1993 l_mean_income_2003->l_mean_income
    S_poor_1983 S_poor_1993 S_poor_2003   ->   S_poor
 S_manuf_1983 S_manuf_1993 S_manuf_2003   ->   S_manuf
 S_truck_1983 S_truck_1993 S_truck_2003   ->   S_truck
       l_pop_1983 l_pop_1993 l_pop_2003   ->   l_pop
-----------------------------------------------------------------------------

. 
.         local geography  "elevat_range_msa ruggedness_msa heating_dd cooling_dd spr
> awl"

.         local demographics "S_somecollege l_mean_income S_poor S_manuf"  

.         local census_div "div1 div2 div3 div4 div5 div6 div7 div8 div9"

.         local population "l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20" 
>  

. 
. 
.         
. 
. 
. *** Decade 1 ('80s) *** 
. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
.                 
. 
. * Model 1 *
.                 
.                 ivregress liml l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_198
> 3", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(1)    =    1020.96
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8506
                                                  Root MSE        =     .53989

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.389311   .0434806    31.95   0.000      1.30409    1.474531
       _cons |   5.805082   .2831024    20.51   0.000     5.250211    6.359952
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.         
. 
.                 genqreg l_vmt l_ln if year == "_1983",  q(10) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.57   .0825113    19.03   0.000     1.408281    1.731719
       _cons |   3.892742   .6501657     5.99   0.000     2.618441    5.167043
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(25) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.47   .0495454    29.67   0.000     1.372893    1.567107
       _cons |   4.977146   .3731905    13.34   0.000     4.245706    5.708586
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(50) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)   
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.4   .0424694    32.96   0.000     1.316762    1.483238
       _cons |     5.7392   .3190699    17.99   0.000     5.113835    6.364565
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(75) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.445   .0660774    21.87   0.000     1.315491    1.574509
       _cons |   5.755566   .4784429    12.03   0.000     4.817836    6.693297
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(90) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.595   .1448235    11.01   0.000     1.311151    1.878849
       _cons |   5.265996   .9605241     5.48   0.000     3.383403    7.148589
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
    Note: Alternative solutions exist. See e(solutions).

. 
.                 
.                 
. * Model 2 *             
.                 
.                 ivregress liml l_vmt l_pop (l_ln = l_rail1898 l_hwy1947) if year ==
>  "_1983", robust                     

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(2)    =    1979.22
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9183
                                                  Root MSE        =     .39931

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.088868   .1029366    10.58   0.000     .8871158     1.29062
       l_pop |    .327336    .075408     4.34   0.000     .1795391    .4751329
       _cons |    3.59226   .4017246     8.94   0.000     2.804894    4.379626
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

. 
. 
.                 genqreg l_vmt l_ln if year == "_1983",  q(10) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.1   .1754583     6.27   0.000      .756108    1.443892
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(25) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)   
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.21   .5898378     2.05   0.040     .0539391    2.366061
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(50) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947)  optimize(grid) grid1(0.1(0.01)2)   
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.29   .1722598     7.49   0.000      .952377    1.627623
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(75) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947)  optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.415   .1727731     8.19   0.000     1.076371    1.753629
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(90) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)       
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.605   .1049411    15.29   0.000     1.399319    1.810681
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.         
.         
.         
. * Model 3 *                     
. 
.                 ivregress liml l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 div9 
> elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln = l_rail1898 l_h
> wy1947) if year == "_1983", robust                                

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(15)   =    3105.38
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9288
                                                  Root MSE        =     .37266

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.183185   .1139465    10.38   0.000     .9598537    1.406516
           l_pop |   .2295255   .0940476     2.44   0.015     .0451956    .4138555
            div2 |  -.2592773   .2079813    -1.25   0.213    -.6669132    .1483585
            div3 |  -.0742992   .2310816    -0.32   0.748    -.5272109    .3786125
            div4 |  -.1661479   .2483188    -0.67   0.503    -.6528439     .320548
            div5 |  -.2057264   .2353327    -0.87   0.382      -.66697    .2555171
            div6 |  -.3651358    .243682    -1.50   0.134    -.8427437     .112472
            div7 |  -.1400862    .254774    -0.55   0.582    -.6394341    .3592616
            div8 |  -.5093132   .2986151    -1.71   0.088    -1.094588    .0759616
            div9 |  -.4096747   .2875629    -1.42   0.154    -.9732877    .1539383
elevat_range_msa |  -.0675598   .0677415    -1.00   0.319    -.2003306     .065211
  ruggedness_msa |    7.87936    4.10107     1.92   0.055    -.1585899    15.91731
      heating_dd |  -.0170092   .0056411    -3.02   0.003    -.0280657   -.0059528
      cooling_dd |  -.0273919   .0121506    -2.25   0.024    -.0512067   -.0035772
          sprawl |   .0012222   .0040156     0.30   0.761    -.0066483    .0090927
           _cons |   5.533433   1.023028     5.41   0.000     3.528334    7.538531
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(10) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.57   .1491412    10.53   0.000     1.277689    1.862311
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(25) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.405   .1389669    10.11   0.000      1.13263     1.67737
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(50) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)   
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.37   .1284779    10.66   0.000     1.118188    1.621812
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(75) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.06   .1334205     7.94   0.000     .7985006    1.321499
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(90) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)   
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .77   .3748166     2.05   0.040      .035373    1.504627
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.         
.         
.         
. * Model 4 *                     
.         
.                 ivregress liml l_vmt l_pop S_somecollege l_mean_income S_poor S_man
> uf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl (l_ln = l_rail1898 l_hwy1947) if year == "_1983", robust      
>                                

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(19)   =    3485.17
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9334
                                                  Root MSE        =     .36041

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.153168   .1308592     8.81   0.000     .8966889    1.409648
           l_pop |   .2637207   .1170578     2.25   0.024     .0342915    .4931498
   S_somecollege |    .676887    .587283     1.15   0.249    -.4741664    1.827941
   l_mean_income |  -.0604615   .5799422    -0.10   0.917    -1.197127    1.076204
          S_poor |   .4201981   .7063741     0.59   0.552    -.9642696    1.804666
         S_manuf |   .3357823   .3840518     0.87   0.382    -.4169454     1.08851
            div2 |  -.2028549   .2213102    -0.92   0.359     -.636615    .2309051
            div3 |  -.0193329   .2614817    -0.07   0.941    -.5318276    .4931618
            div4 |  -.1493699   .2644041    -0.56   0.572    -.6675924    .3688525
            div5 |  -.1822386   .2350294    -0.78   0.438    -.6428877    .2784105
            div6 |  -.3573986   .2644655    -1.35   0.177    -.8757414    .1609443
            div7 |  -.1116564   .2830879    -0.39   0.693    -.6664985    .4431856
            div8 |  -.4417695   .3133119    -1.41   0.159     -1.05585    .1723107
            div9 |  -.3188542   .2945992    -1.08   0.279     -.896258    .2585496
elevat_range_msa |  -.0754879   .0627373    -1.20   0.229    -.1984509     .047475
  ruggedness_msa |   7.316705   3.740218     1.96   0.050     -.013988     14.6474
      heating_dd |  -.0153037   .0057414    -2.67   0.008    -.0265566   -.0040508
      cooling_dd |  -.0221265   .0119574    -1.85   0.064    -.0455625    .0013095
          sprawl |   .0020474   .0042186     0.49   0.627    -.0062208    .0103156
           _cons |   5.261053   5.030754     1.05   0.296    -4.599044    15.12115
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(10) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.605   .1256685    12.77   0.000     1.358694    1.851306
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(25) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2)  
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.37   .2484904     5.51   0.000     .8829677    1.857032
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(50) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2)  
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.26   .1601808     7.87   0.000     .9460515    1.573949
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(75) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.06   .1821503     5.82   0.000      .702992    1.417008
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(90) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2)       
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.03   .0923098    11.16   0.000     .8490761    1.210924
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl

. 
.                 
.                 
.                 
. * Model 5 *                             
.         
.                 ivregress liml l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 
> l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
>  div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln 
> = l_rail1898 l_hwy1947) if year == "_1983", robust                                 
>     
note: l_pop80 omitted because of collinearity.

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(25)   =    3564.51
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9294
                                                  Root MSE        =     .37116

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.209407   .1560418     7.75   0.000     .9035702    1.515243
           l_pop |   .8076052   .3975979     2.03   0.042     .0283277    1.586883
         l_pop80 |          0  (omitted)
         l_pop70 |  -.2933245   .7136873    -0.41   0.681    -1.692126    1.105477
         l_pop60 |  -.2562645   .5763745    -0.44   0.657    -1.385938    .8734088
         l_pop50 |   -.052708   .3657762    -0.14   0.885    -.7696161    .6642001
         l_pop40 |  -.2003127   .4060961    -0.49   0.622    -.9962465    .5956211
         l_pop30 |   .2940406   .3098573     0.95   0.343    -.3132686    .9013498
         l_pop20 |  -.0710513   .1380884    -0.51   0.607    -.3416996     .199597
   S_somecollege |   .0005674   .8781519     0.00   0.999    -1.720579    1.721714
   l_mean_income |   .1600142   .6332394     0.25   0.801    -1.081112    1.401141
          S_poor |   .7826639   .7880007     0.99   0.321    -.7617891    2.327117
         S_manuf |   .3835071   .4082705     0.94   0.348    -.4166883    1.183702
            div2 |  -.2694866    .265279    -1.02   0.310    -.7894238    .2504506
            div3 |  -.1039546    .314631    -0.33   0.741    -.7206201    .5127109
            div4 |  -.1827818   .2984389    -0.61   0.540    -.7677114    .4021477
            div5 |  -.2647555   .2790505    -0.95   0.343    -.8116845    .2821734
            div6 |    -.42869   .3072031    -1.40   0.163    -1.030797    .1734171
            div7 |   -.139173   .3174915    -0.44   0.661     -.761445    .4830989
            div8 |  -.5764346   .3820095    -1.51   0.131    -1.325159    .1722902
            div9 |  -.4010393   .3629437    -1.10   0.269    -1.112396    .3103173
elevat_range_msa |  -.0949136   .0625016    -1.52   0.129    -.2174144    .0275872
  ruggedness_msa |   6.426562    3.75422     1.71   0.087    -.9315742     13.7847
      heating_dd |   -.017303   .0063722    -2.72   0.007    -.0297923   -.0048137
      cooling_dd |  -.0348996   .0137612    -2.54   0.011    -.0618711   -.0079281
          sprawl |   .0008905   .0047222     0.19   0.850    -.0083649    .0101459
           _cons |   3.551531   5.358626     0.66   0.507    -6.951183    14.05424
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege
            l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9
            elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
            l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(10) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.58   .1285908    12.29   0.000     1.327967    1.832033
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(25) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947)  optimize(grid) gri
> d1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.405   .1161182    12.10   0.000     1.177412    1.632588
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(50) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947)  optimize(grid) gri
> d1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.3   .1281175    10.15   0.000     1.048894    1.551106
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(75) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947)  optimize(grid) gri
> d1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |         .7   .1365399     5.13   0.000     .4323867    .9676133
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1983",  q(90) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2)       
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.29   .0895043    14.41   0.000     1.114575    1.465425
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

.         
.         
.         
.         
.         
. *** Decade 2 ('90s) *** 
. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
.                 
.                 ivregress liml l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_199
> 3", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(1)    =     737.22
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8644
                                                  Root MSE        =     .47683

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.333483    .049112    27.15   0.000     1.237225    1.429741
       _cons |   6.563724   .3314169    19.81   0.000     5.914159    7.213289
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

. 
.                 genqreg l_vmt l_ln if year == "_1993",  q(10) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.615   .0702057    23.00   0.000     1.477399    1.752601
       _cons |   3.953529    .593691     6.66   0.000     2.789916    5.117142
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(25) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)  
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.385   .0487587    28.41   0.000     1.289435    1.480565
       _cons |   6.021358   .4021678    14.97   0.000     5.233124    6.809593
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(50) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.28   .0402849    31.77   0.000     1.201043    1.358957
       _cons |   7.004383   .3298574    21.23   0.000     6.357875    7.650892
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(75) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.36   .0643333    21.14   0.000     1.233909    1.486091
       _cons |   6.688542   .4522834    14.79   0.000     5.802083    7.575001
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(90) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.395   .1082328    12.89   0.000     1.182868    1.607132
       _cons |    6.71479   .6698621    10.02   0.000     5.401884    8.027695
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 2 *     
. 
.                 ivregress liml l_vmt l_pop (l_ln = l_rail1898 l_hwy1947) if year ==
>  "_1993", robust                     

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(2)    =    2268.59
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9265
                                                  Root MSE        =     .35102

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .9750698   .1281312     7.61   0.000     .7239374    1.226202
       l_pop |   .3682688   .0958781     3.84   0.000     .1803512    .5561863
       _cons |   4.202293    .462304     9.09   0.000     3.296194    5.108392
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

. 
.                 genqreg l_vmt l_ln if year == "_1993",  q(10) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.35   .2055116     6.57   0.000     .9472047    1.752795
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(25) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.015   .2647271     3.83   0.000     .4961445    1.533855
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(50) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.25   .1629606     7.67   0.000     .9306031    1.569397
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(75) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.04   .0969079    10.73   0.000      .850064    1.229936
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(90) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)            
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .98   .2813907     3.48   0.000     .4284843    1.531516
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
. 
. 
. 
. * Model 3 *     
. 
.                 ivregress liml l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 div9 
> elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln = l_rail1898 l_h
> wy1947) if year == "_1993", robust                                

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(15)   =    4016.76
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9343
                                                  Root MSE        =     .33189

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.127834   .1608668     7.01   0.000     .8125409    1.443127
           l_pop |   .2126386   .1298594     1.64   0.102    -.0418811    .4671583
            div2 |  -.2047742   .1030457    -1.99   0.047    -.4067402   -.0028083
            div3 |   .0368745   .1107627     0.33   0.739    -.1802163    .2539654
            div4 |  -.1130231   .1407965    -0.80   0.422    -.3889791     .162933
            div5 |  -.0815081   .1413372    -0.58   0.564    -.3585238    .1955077
            div6 |  -.2018811   .1443589    -1.40   0.162    -.4848193    .0810572
            div7 |  -.2466525   .1624922    -1.52   0.129    -.5651313    .0718263
            div8 |  -.5230422   .2259541    -2.31   0.021    -.9659042   -.0801803
            div9 |  -.3902554   .2059464    -1.89   0.058    -.7939029     .013392
elevat_range_msa |  -.0105343   .0650254    -0.16   0.871    -.1379817     .116913
  ruggedness_msa |   6.723697   3.698433     1.82   0.069    -.5250991    13.97249
      heating_dd |  -.0160764   .0047356    -3.39   0.001     -.025358   -.0067947
      cooling_dd |  -.0249773   .0093188    -2.68   0.007    -.0432417   -.0067129
          sprawl |  -.0009375   .0034764    -0.27   0.787    -.0077511    .0058761
           _cons |   6.440649   .9589687     6.72   0.000     4.561104    8.320193
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(10) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.57   .1168164    13.44   0.000     1.341044    1.798956
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(25) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.305   .1855375     7.03   0.000     .9413531    1.668647
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(50) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.24   .1184903    10.46   0.000     1.007763    1.472237
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(75) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .655   .1207901     5.42   0.000     .4182557    .8917443
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(90) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)   
>                     
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .895    1.20827     0.74   0.459    -1.473166    3.263166
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 4 *
. 
.                 ivregress liml l_vmt l_pop S_somecollege l_mean_income S_poor S_man
> uf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl (l_ln = l_rail1898 l_hwy1947) if year == "_1993", robust      
>                                

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(19)   =    5064.85
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9433
                                                  Root MSE        =     .30828

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |    1.08299   .1529358     7.08   0.000     .7832408    1.382738
           l_pop |   .2895093   .1173025     2.47   0.014     .0596007    .5194179
   S_somecollege |   1.110934     .41008     2.71   0.007     .3071922    1.914676
   l_mean_income |   -.818776   .3668535    -2.23   0.026    -1.537796   -.0997564
          S_poor |  -.2570786   .6672814    -0.39   0.700    -1.564926    1.050769
         S_manuf |   1.026007   .4642586     2.21   0.027     .1160765    1.935937
            div2 |  -.1867884   .1128189    -1.66   0.098    -.4079095    .0343327
            div3 |   .0649713   .1251085     0.52   0.604    -.1802368    .3101794
            div4 |  -.1293361   .1447523    -0.89   0.372    -.4130454    .1543732
            div5 |  -.0997413   .1382738    -0.72   0.471    -.3707529    .1712704
            div6 |  -.2336726    .151499    -1.54   0.123    -.5306052      .06326
            div7 |  -.1967181   .1655985    -1.19   0.235    -.5212852    .1278491
            div8 |  -.3935185   .1935199    -2.03   0.042    -.7728106   -.0142264
            div9 |   -.254319   .1811528    -1.40   0.160    -.6093719    .1007339
elevat_range_msa |  -.0475659   .0558235    -0.85   0.394    -.1569779    .0618461
  ruggedness_msa |     7.2235   3.400976     2.12   0.034     .5577098    13.88929
      heating_dd |  -.0127365   .0044775    -2.84   0.004    -.0215122   -.0039608
      cooling_dd |  -.0161507   .0097277    -1.66   0.097    -.0352166    .0029153
          sprawl |  -.0006681   .0033692    -0.20   0.843    -.0072716    .0059355
           _cons |   12.96237   3.471291     3.73   0.000     6.158768    19.76598
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(10) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.57   .1212985    12.94   0.000     1.332259    1.807741
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(25) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.305   .1852555     7.04   0.000      .941906    1.668094
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(50) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.24   .1126894    11.00   0.000     1.019133    1.460867
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl

.         
.                 genqreg l_vmt l_ln if year == "_1993",  q(75) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .195   .2194914     0.89   0.374    -.2351952    .6251952
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(90) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .935   .2453944     3.81   0.000     .4540359    1.415964
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
. 
. 
. 
. * Model 5 *     
. 
.                 ivregress liml l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 
> l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
>  div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln 
> = l_rail1898 l_hwy1947) if year == "_1993", robust                                 
>     

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(26)   =    5359.22
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9431
                                                  Root MSE        =     .30888

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.144559   .1685979     6.79   0.000     .8141136    1.475005
           l_pop |   .0261896   .3319801     0.08   0.937    -.6244794    .6768586
         l_pop80 |   .9193203   .5807054     1.58   0.113    -.2188414    2.057482
         l_pop70 |  -.0027197   .6152809    -0.00   0.996    -1.208648    1.203209
         l_pop60 |  -.8305019   .4989422    -1.66   0.096    -1.808411    .1474068
         l_pop50 |   .0084253   .3336952     0.03   0.980    -.6456052    .6624559
         l_pop40 |  -.0074528   .3626622    -0.02   0.984    -.7182576    .7033521
         l_pop30 |   .1392354   .2835559     0.49   0.623     -.416524    .6949947
         l_pop20 |   -.003821   .1389434    -0.03   0.978    -.2761451    .2685031
   S_somecollege |    .362046   .5470456     0.66   0.508    -.7101436    1.434236
   l_mean_income |  -.7038179   .3746285    -1.88   0.060    -1.438076    .0304405
          S_poor |   .0951441   .6919842     0.14   0.891     -1.26112    1.451408
         S_manuf |   .9234655   .4445528     2.08   0.038      .052158    1.794773
            div2 |  -.1975844    .116514    -1.70   0.090    -.4259477    .0307789
            div3 |   .0238583   .1334998     0.18   0.858    -.2377966    .2855132
            div4 |  -.1170409   .1453341    -0.81   0.421    -.4018906    .1678088
            div5 |  -.1569818    .148658    -1.06   0.291    -.4483462    .1343826
            div6 |  -.2779283   .1597759    -1.74   0.082    -.5910832    .0352267
            div7 |  -.1831058   .1754858    -1.04   0.297    -.5270517      .16084
            div8 |  -.4765189   .2292798    -2.08   0.038    -.9258991   -.0271387
            div9 |  -.2611827   .2213351    -1.18   0.238    -.6949916    .1726262
elevat_range_msa |  -.0625092   .0562626    -1.11   0.267    -.1727818    .0477635
  ruggedness_msa |   4.858655   3.573066     1.36   0.174    -2.144426    11.86174
      heating_dd |  -.0151378   .0047742    -3.17   0.002     -.024495   -.0057805
      cooling_dd |  -.0288267   .0107574    -2.68   0.007    -.0499109   -.0077425
          sprawl |   -.001524   .0036019    -0.42   0.672    -.0085836    .0055356
           _cons |    12.4732   3.654967     3.41   0.001     5.309591     19.6368
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(10) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2)               
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.52    .130675    11.63   0.000     1.263882    1.776118
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(25) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947)   optimize(grid) gr
> id1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.28   .1918756     6.67   0.000     .9039307    1.656069
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(50) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947)  optimize(grid) gri
> d1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.24   .1200306    10.33   0.000     1.004744    1.475256
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(75) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947)   optimize(grid) gr
> id1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .195   .2190548     0.89   0.373    -.2343395    .6243395
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_1993",  q(90) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .62   2.295535     0.27   0.787    -3.879165    5.119165
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

. 
.                 
. 
. 
. 
. *** Decade 3 ('00s) *** 
. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
. 
. 
. * Model 1 *     
. 
.                 ivregress liml l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_200
> 3", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(1)    =     705.86
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8759
                                                  Root MSE        =     .45051

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.258871   .0473829    26.57   0.000     1.166002    1.351739
       _cons |   7.321904   .3226186    22.70   0.000     6.689584    7.954225
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

. 
.                 genqreg l_vmt l_ln if year == "_2003",  q(10) instruments(l_rail189
> 8 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.46   .0519237    28.12   0.000     1.358231    1.561769
       _cons |   5.391166   .4292987    12.56   0.000     4.549756    6.232576
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(25) instruments(l_rail189
> 8 l_hwy1947)  optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        1.4   .0506687    27.63   0.000     1.300691    1.499309
       _cons |    6.16549   .3864117    15.96   0.000     5.408137    6.922843
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(50) instruments(l_rail189
> 8 l_hwy1947)  optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.25   .0425713    29.36   0.000     1.166562    1.333438
       _cons |   7.449626   .3938724    18.91   0.000     6.677651    8.221602
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(75) instruments(l_rail189
> 8 l_hwy1947)  optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.26    .047338    26.62   0.000     1.167219    1.352781
       _cons |    7.60893   .3556295    21.40   0.000     6.911909    8.305952
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(90) instruments(l_rail189
> 8 l_hwy1947)  optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.27   .0605104    20.99   0.000     1.151402    1.388598
       _cons |   7.701235   .3984509    19.33   0.000     6.920286    8.482184
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947

.                 
. 
. 
. 
. * Model 2 *     
. 
.                 ivregress liml l_vmt l_pop (l_ln = l_rail1898 l_hwy1947) if year ==
>  "_2003", robust                     

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(2)    =    2625.85
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9388
                                                  Root MSE        =     .31648

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .8165659   .1143325     7.14   0.000     .5924783    1.040654
       l_pop |   .4541329   .0859888     5.28   0.000     .2855981    .6226678
       _cons |   4.376733   .4235033    10.33   0.000     3.546681    5.206784
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

. 
.                 genqreg l_vmt l_ln if year == "_2003",  q(10) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.015   .2348138     4.32   0.000     .5547733    1.475227
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(25) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .97    .493155     1.97   0.049     .0034339    1.936566
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(50) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.14   .4317812     2.64   0.008     .2937244    1.986276
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(75) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .485   .1110112     4.37   0.000      .267422     .702578
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(90) proneness(l_pop) inst
> ruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)               
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.01   .2239262     4.51   0.000     .5711128    1.448887
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop

. 
. 
. * Model 3 *     
. 
.                 ivregress liml l_vmt l_pop div2 div3 div4 div5 div6 div7 div8 div9 
> elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln = l_rail1898 l_h
> wy1947) if year == "_2003", robust                                

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(15)   =    4991.34
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9507
                                                  Root MSE        =     .28406

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9286222   .1276992     7.27   0.000     .6783363    1.178908
           l_pop |   .3455688   .1029213     3.36   0.001     .1438468    .5472907
            div2 |  -.1900602   .0920864    -2.06   0.039    -.3705463   -.0095741
            div3 |   .0086584   .0985216     0.09   0.930    -.1844405    .2017573
            div4 |  -.0522848   .1212526    -0.43   0.666    -.2899356     .185366
            div5 |  -.0589823   .1295412    -0.46   0.649    -.3128785    .1949138
            div6 |  -.1243033   .1346421    -0.92   0.356    -.3881969    .1395904
            div7 |  -.2006758   .1455615    -1.38   0.168    -.4859711    .0846196
            div8 |  -.4006379   .1732224    -2.31   0.021    -.7401475   -.0611282
            div9 |  -.3898768   .1762187    -2.21   0.027     -.735259   -.0444946
elevat_range_msa |  -.0082302   .0553744    -0.15   0.882     -.116762    .1003016
  ruggedness_msa |   6.035588   3.324306     1.82   0.069    -.4799323    12.55111
      heating_dd |  -.0134167   .0040487    -3.31   0.001    -.0213519   -.0054814
      cooling_dd |  -.0202258   .0083641    -2.42   0.016    -.0366192   -.0038324
          sprawl |   .0004941   .0027953     0.18   0.860    -.0049845    .0059727
           _cons |   6.007039   .7711651     7.79   0.000     4.495583    7.518495
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa
            ruggedness_msa heating_dd cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(10) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)  
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.445   .0933829    15.47   0.000     1.261973    1.628027
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(25) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.13   .8323465     1.36   0.175    -.5013691    2.761369
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(50) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.06   .2196235     4.83   0.000     .6295459    1.490454
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(75) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .72   .1116621     6.45   0.000     .5011464    .9388536
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(90) proneness(l_pop div2 
> div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooli
> ng_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid1(0.1(0.01)2)   
>                     
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .68    .540318     1.26   0.208    -.3790037    1.739004
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa r
> uggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 4 *     
. 
.                 ivregress liml l_vmt l_pop S_somecollege l_mean_income S_poor S_man
> uf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl (l_ln = l_rail1898 l_hwy1947) if year == "_2003", robust      
>                                

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(19)   =    5830.03
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9556
                                                  Root MSE        =     .26967

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9233655   .1290701     7.15   0.000     .6703929    1.176338
           l_pop |   .3658209   .1015085     3.60   0.000     .1668679    .5647739
   S_somecollege |   1.076834    .309514     3.48   0.001     .4701978     1.68347
   l_mean_income |  -.7171055   .2887513    -2.48   0.013    -1.283048   -.1511634
          S_poor |  -1.461358   .7057516    -2.07   0.038    -2.844605   -.0781101
         S_manuf |   .8917814   .5219662     1.71   0.088    -.1312536    1.914816
            div2 |  -.1461299   .1003785    -1.46   0.145    -.3428681    .0506083
            div3 |      .0277   .1143706     0.24   0.809    -.1964622    .2518622
            div4 |  -.0841042   .1232377    -0.68   0.495    -.3256457    .1574373
            div5 |   -.126117   .1299113    -0.97   0.332    -.3807384    .1285044
            div6 |  -.1713554   .1387151    -1.24   0.217    -.4432321    .1005213
            div7 |  -.1440147   .1525284    -0.94   0.345    -.4429649    .1549355
            div8 |  -.3406411   .1587186    -2.15   0.032    -.6517237   -.0295584
            div9 |  -.2815889   .1695661    -1.66   0.097    -.6139324    .0507546
elevat_range_msa |  -.0311476   .0489088    -0.64   0.524    -.1270071     .064712
  ruggedness_msa |   6.168454   3.045415     2.03   0.043      .199551    12.13736
      heating_dd |  -.0130816   .0039166    -3.34   0.001     -.020758   -.0054052
      cooling_dd |   -.015671   .0087907    -1.78   0.075    -.0329005    .0015585
          sprawl |   .0009868    .002687     0.37   0.713    -.0042795    .0062531
           _cons |   12.27195   2.675076     4.59   0.000     7.028895      17.515
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(10) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.445   .1014988    14.24   0.000     1.246066    1.643934
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(25) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.13   .5691478     1.99   0.047     .0144908    2.245509
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(50) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.06   .2501479     4.24   0.000     .5697191    1.550281
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(75) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .615   .1977881     3.11   0.002     .2273425    1.002657
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(90) proneness(l_pop S_som
> ecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 eleva
> t_range_msa ruggedness_msa heating_dd cooling_dd sprawl) instruments(l_rail1898 l_h
> wy1947) optimize(grid) grid1(0.1(0.01)2)                       
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .105    1.07473     0.10   0.922    -2.001432    2.211432
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 
> div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd spra
> wl
    Note: Alternative solutions exist. See e(solutions).

. 
. 
. 
. * Model 5 *     
. 
.                 ivregress liml l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 
> l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
>  div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln 
> = l_rail1898 l_hwy1947) if year == "_2003", robust                                 
>     

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(26)   =    6236.55
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9564
                                                  Root MSE        =     .26707

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9672581   .1472585     6.57   0.000     .6786368    1.255879
           l_pop |   .1173743   .1886499     0.62   0.534    -.2523727    .4871213
         l_pop80 |   .8828435   .4907168     1.80   0.072    -.0789437    1.844631
         l_pop70 |  -.1722967   .5757381    -0.30   0.765    -1.300723    .9561292
         l_pop60 |  -.6157573   .4215833    -1.46   0.144    -1.442045    .2105307
         l_pop50 |   .0103154   .2454949     0.04   0.966    -.4708457    .4914766
         l_pop40 |   .0417089   .2633938     0.16   0.874    -.4745335    .5579512
         l_pop30 |   .0595162   .2498483     0.24   0.812    -.4301775    .5492098
         l_pop20 |   .0153688   .1268345     0.12   0.904    -.2332223    .2639599
   S_somecollege |   .7194102   .3658649     1.97   0.049     .0023282    1.436492
   l_mean_income |    -.62401   .3168861    -1.97   0.049    -1.245095   -.0029248
          S_poor |  -.9611691   .7440869    -1.29   0.196    -2.419553    .4972144
         S_manuf |   .8908962   .5131374     1.74   0.083    -.1148345    1.896627
            div2 |  -.1569872   .1008926    -1.56   0.120    -.3547331    .0407586
            div3 |  -.0004958   .1170454    -0.00   0.997    -.2299005    .2289089
            div4 |  -.0737767   .1209079    -0.61   0.542    -.3107519    .1631984
            div5 |  -.1706845   .1309558    -1.30   0.192    -.4273532    .0859842
            div6 |  -.2087752   .1392332    -1.50   0.134    -.4816673    .0641169
            div7 |  -.1649149   .1543327    -1.07   0.285    -.4674014    .1375717
            div8 |  -.4123736    .200331    -2.06   0.040    -.8050151   -.0197321
            div9 |  -.3199722    .208916    -1.53   0.126    -.7294401    .0894958
elevat_range_msa |  -.0302601   .0488388    -0.62   0.536    -.1259824    .0654623
  ruggedness_msa |   4.302038   3.261091     1.32   0.187    -2.089582    10.69366
      heating_dd |  -.0148555   .0041291    -3.60   0.000    -.0229485   -.0067625
      cooling_dd |  -.0234122   .0094586    -2.48   0.013    -.0419507   -.0048737
          sprawl |   .0000809   .0028467     0.03   0.977    -.0054985    .0056603
           _cons |   11.74726   3.038519     3.87   0.000     5.791868    17.70264
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl l_rail1898 l_hwy1947

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(10) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.42   .0950359    14.94   0.000     1.233733    1.606267
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(25) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |      1.325   .1693222     7.83   0.000     .9931345    1.656866
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(50) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       1.09    .223336     4.88   0.000     .6522695    1.527731
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(75) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2) 
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |       .255    .211253     1.21   0.227    -.1590482    .6690482
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                 
.                 genqreg l_vmt l_ln if year == "_2003",  q(90) proneness(l_pop l_pop
> 80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_po
> or S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa 
> heating_dd cooling_dd sprawl) instruments(l_rail1898 l_hwy1947) optimize(grid) grid
> 1(0.1(0.01)2)       
Grid-search optimization
Number of grid points: 191
 
Generalized Quantile Regression (GQR)
     Observations:                228
 
------------------------------------------------------------------------------
       l_vmt | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |        .48   .6218253     0.77   0.440    -.7387553    1.698755
------------------------------------------------------------------------------
Excluded intstruments: l_rail1898 l_hwy1947
Proneness variables: l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_
> somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 el
> evat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
    Note: Alternative solutions exist. See e(solutions).

.                         
.         
. 
. log off 
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_3.log
  log type:  text
 paused on:  11 May 2023, 12:39:27
-------------------------------------------------------------------------------------
